a135410369
Packed-function initialization callbacks already use internal linkage. Marking them hidden additionally causes LLVM ORCJIT to reject the resulting private-external Mach-O symbols as unexpected definitions. This PR removes the redundant hidden visibility. |
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README.md
Open Machine Learning Compiler Framework
Documentation | Contributors | Community | Release Notes
Apache TVM is an open machine learning compilation framework, following the following principles:
- Python-first development that enables quick customization of machine learning compiler pipelines.
- Universal deployment to bring models into minimum deployable modules.
License
TVM is licensed under the Apache-2.0 license.
Getting Started
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
Contribute to TVM
TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community. Check out the Contributor Guide.
History and Acknowledgement
TVM started as a research project for deep learning compilation. The first version of the project benefited a lot from the following projects:
- Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.
- Loopy: use of integer set analysis and its loop transformation primitives.
- Theano: the design inspiration of symbolic scan operator for recurrence.
Since then, the project has gone through several rounds of redesigns. The current design is also drastically different from the initial design, following the development trend of the ML compiler community.
The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation and Relax as the graph-level representation and Python-first transformations. The project's current design goal is to make the ML compiler accessible by enabling most transformations to be customizable in Python and bringing a cross-level representation that can jointly optimize computational graphs, tensor programs, and libraries. The project is also a foundation infra for building Python-first vertical compilers for domains, such as LLMs.